I have an array Nx3 of N points, each one has X, Y and Z coordinate. I need to rotate each point, so i have the rotation matrix 3×3. To do this, i need to get dot product of the rotation matrix and each point. The problem is the array of points is quite massive (~1_000_000x3) and therefore it takes
Tag: numpy
Remove the intersection between two curves
I’m having a curve (parabol) from 0 to 1 on both axes as follows: I generate another curve by moving the original curve along the x-axis and combine both to get the following graph: How can I remove the intersected section to have only the double bottoms pattern like this: The code I use for the graph: I’m doing a
Sum the predictions of a Linear Regression from Scikit-Learn
I need to make a linear regression and sum all the predictions. Maybe this isn’t a question for Scikit-Learn but for NumPy because I get an array at the end and I am unable to turn it into a float. I am getting it right up to this point. The next part (which is a while loop to sum all
how to drop rows with ‘nan’ in a column in a pandas dataframe?
I have a dataframe (denoted as ‘df’) where some values are missing in a column (denoted as ‘col1’). I applied a set function to find unique values in the column: I am trying to drop these ‘nan’ rows from the dataframe where I have tried this: However, the column rows remain unchanged. I’m thinking that the above repeated ‘nan’ values
Is there a way to write a python function that will create ‘N’ arrays? (see body)
I have an numpy array that is shape 20, 3. (So 20 3 by 1 arrays. Correct me if I’m wrong, I am still pretty new to python) I need to separate it into 3 arrays of shape 20,1 where the first array is 20 elements that are the 0th element of each 3 by 1 array. Second array is
Exponential fit in pandas
I have this data: The data seems to follow an exponential curve. Let’s see the plot: I want to fit an exponential curve ($$ y = Ae^{Bx} $$, A times e to the B*X)and add it as a column in Pandas. Firstly I tried to log the values: And then to use Numpy to fit the equation: But I get
Appending an empty list to a numpy array changes its dtype
I have a numpy array of integers. In my code I need to append some other integers from a list, which works fine and gives me back an array of dtype int64 as expected. But it may happen that the list of integers to append is empty. In that case, numpy returns an array of float64 values. Exemplary code below:
python) update value of array in dictionary and update it
I want to update ‘array’ inside the dictionary after doing interpolation. for example, “array_ex” is a dictionary and has values like below and ‘0_array’ has (6,100) shape while ‘1_array’ has (6,200) shape… I wrote a function for interpolating the array using np.interp. The function interpolates the shape of array (6,100) to (6,200). However, how can I update my array after
python print array inside the dictionary
I want to print ‘array’ inside the dictionary but my code gives me ‘each value’ of the array. for example, “array_ex” is a dictionary and has values like below with 12 rows for each array… and I want to get each row of the array as a result. However, my code returns each value of the array. How can I
numpy array –> sort descending unique values base count of values
I have a numpy array: I want the same unique array back, but sorted descending in the order of occurrences of the elements. In this example, 6 occurs 3 times, 1 occurs 2 times and all the other elements only occur 1 time, so the result should be: Does anyone know how this can be done? Thanks in advance! Answer